# Copyright (c) OpenMMLab. All rights reserved.
import time
import unittest
from unittest import TestCase

import torch
from mmengine.logging import MessageHub
from mmengine.registry import init_default_scope
from parameterized import parameterized

from mmdet.registry import MODELS
from mmdet.testing import demo_track_inputs, get_detector_cfg


class TestDeepSORT(TestCase):

    @classmethod
    def setUpClass(cls):
        init_default_scope('mmdet')

    @parameterized.expand([
        'sort/sort_faster-rcnn_r50_fpn_8xb2-4e'
        '_mot17halftrain_test-mot17halfval.py'
    ])
    def test_init(self, cfg_file):
        model = get_detector_cfg(cfg_file)
        model = MODELS.build(model)
        assert model.detector
        assert model.tracker

    @parameterized.expand([
        ('sort/sort_faster-rcnn_r50_fpn_8xb2-4e'
         '_mot17halftrain_test-mot17halfval.py', ('cpu', 'cuda')),
    ])
    def test_deepsort_forward_predict_mode(self, cfg_file, devices):
        message_hub = MessageHub.get_instance(
            f'test_deepsort_forward_predict_mode-{time.time()}')
        message_hub.update_info('iter', 0)
        message_hub.update_info('epoch', 0)

        assert all([device in ['cpu', 'cuda'] for device in devices])

        for device in devices:
            _model = get_detector_cfg(cfg_file)
            model = MODELS.build(_model)

            if device == 'cuda':
                if not torch.cuda.is_available():
                    return unittest.skip('test requires GPU and torch+cuda')
                model = model.cuda()

            packed_inputs = demo_track_inputs(
                batch_size=1,
                num_frames=2,
                image_shapes=[(3, 256, 256)],
                num_classes=1)
            out_data = model.data_preprocessor(packed_inputs, False)

            # Test forward test
            model.eval()
            with torch.no_grad():
                batch_results = model.forward(**out_data, mode='predict')
                assert len(batch_results) == 1
